Applying a random forest method approach to model travel mode choice behavior

By Long Cheng, Xuewu Chen, J. De Vos, Xinjun Lai, Frank Witlox. Travel Behaviour & Society, 2019

452 citations recorded by the source corpus.

Abstract The analysis of travel mode choice is important in transportation planning and policy-making in order to understand and forecast travel demands. Research in the field of machine learning has been exploring the use of random forest as a framework within which many traffic and transport problems can be investigated. The random forest (RF) is a powerful method for constructing an ensemble of random decision trees. It de-correlates the decision trees in the ensemble via randomization that leads to an improvement of forecasting and reduces the variance when averaged over the trees. However, the usefulness of RF for travel mode choice behavior remains largely unexplored. This paper proposes a robust random forest method to analyze travel mode choices for examining the prediction capability and model interpretability. Using the travel diary data from Nanjing, China in 2013, enriched with variables on the built environment, the effects of different model parameters on the prediction performance are investigated. The comparison results show that the random forest method performs significantly better in travel mode choice prediction for higher accuracy and less computation cost. In addition, the proposed method estimates the relative importance of explanatory variables and how they relate to mode choices. This is fundamental for a better understanding and effective modeling of people’s travel behavior.

Subfields: Transportation

View Paper: https://www.semanticscholar.org/paper/55bd6aa0d59fd34a833284bce5fc6b81e2b74172

How researchers see this paper

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In Transportation

1. Xiang Yan
2. Alan Yu
3. Xilei Zhao
4. Pascal Van Hentenryck

Citations in the corpus

• Prediction and behavioral analysis of travel mode choice: A comparison of machine learning and logit models (2020, methodology): “For example, Hagenauer and Helbich (2017) compared the variable importance results of machine-learning methods with MNL. Cheng et al. (2019) applied variable importance to evaluate the relative importance of explanatory variables of the RF model to help provide important insights into formulating tr”